activity
20242026
collaborators

5 papers

eess.SP2026

Latent variable models for simultaneous EOV identification and removal in population-based SHM

M. D. Champneys, M. R. Jones, A. J. Hughes +3

The robust treatment of environmental and operational variability (EOV) is an open challenge in population-based structural health monitoring (PBSHM). The difficulty is compounded…

cs.LG2025

Inter-turbine Modelling of Wind-Farm Power using Multi-task Learning

Simon M. Brealy, Lawrence A. Bull, Pauline Beltrando +3

Because of the global need to increase power production from renewable energy resources, developments in the online monitoring of the associated infrastructure is of interest to re…

cs.LG2025

Regularising NARX models with multi-task learning

Sarah Bee, Lawrence Bull, Nikolaos Dervilis +1

A Nonlinear Auto-Regressive with eXogenous inputs (NARX) model can be used to describe time-varying processes; where the output depends on both previous outputs and current/previou…

cs.LG2024

When does a bridge become an aeroplane?

Tina A. Dardeno, Lawrence A. Bull, Nikolaos Dervilis +1

Despite recent advances in population-based structural health monitoring (PBSHM), knowledge transfer between highly-disparate structures (i.e., heterogeneous populations) remains a…

stat.ML2024

On the topology and geometry of population-based SHM

Keith Worden, Tina A. Dardeno, Aidan J. Hughes +1

Population-Based Structural Health Monitoring (PBSHM), aims to leverage information across populations of structures in order to enhance diagnostics on those with sparse data. The…